Instructions

• Activate raster and rgdal library
• Use GDALinfo() to inspect the properties of the Aster image raster in the data folder (the file name is “Aster.tif”). What can you learn from this inspection about its bands, resolution, and projection?
• Read in the Aster image. Review Week 02 raster loading if unsure about how.
• Use the inMemory() function on the elevation object to determine if the data has been read in.
• Use the head() function to look at the first few values from the elevation raster.
• Use the getValues() function on the elevation object to read in all the data.
• Use the hist() function to create a quick histogram of the elevation values. Note the pile of values near -9999, these should be NA (any idea why?) and we will address this later.

# Library
library(raster)
library(rgdal)

# Read in the elevation layer
elevation <- raster("C:/Users/alexp/Documents/School/Spatial_analytics/HW/HW3/cds-spatial/data/Aster.tif")

# Check if the data is in memory
inMemory(elevation)

[1] FALSE

# Use head() to peak at the first few records
head(elevation)

       1     2     3     4     5     6     7     8     9    10    11    12
1  -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999
2  -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999
3  -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999
      13    14    15    16    17    18    19    20
1  -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999
2  -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999
3  -9999 -9999 -9999 -9999 -9999 -9999 -9999 -9999
 [ reached getOption("max.print") -- omitted 7 rows ]

# Use getValues() to read the values into a vector
vals <- getValues(elevation)

# Use hist() to create a histogram of the values
hist(vals)

# all I could find is that 'the Values of -9999 are assigned to missing or
# cloudy data' taken from
# https://lpdaac.usgs.gov/documents/120/ASTERGED_User_Guide_V3.pdf also '-9999
# for void pixels and 0 for sea-level water body' from
# https://lpdaac.usgs.gov/documents/434/ASTGTM_User_Guide_V3.pdf

Instructions

• Check that the package raster and the object elevation are loaded in your workspace.
• Plot the elevation raster using plot().
• Set up a three-column matrix with the cbind() function and values -10000, 0, NA.
• Use the matrix and reclassify() to reclassify values below 0 to NA. You will need to use the argument rcl.
• Plot the reclassified elevation layer to confirm there are no values below 0.

# Plot the elevation layer to see the legend values
plot(elevation)

# Set up the matrix
vals <- cbind(-10000, 0, NA)

# Reclassify
elevation_reclass <- reclassify(elevation, rcl = vals)

# Plot again and confirm that the legend range is 0 - 2400
plot(elevation_reclass)

Instructions I

• Load sf library
• Create mounds object from the shapefile “KAZ_mounds.shp”. For a refresher on vector loading, check out Week 02 instructions.
• Verify that mounds CRS matches the elevation and fix with st_transform() if not.
• Create a bounding box around the mounds with st_make_grid() following Week 03 guidelines.
• Plot the mounds_bb and the mounds to see how these two objects relate.
• Crop the elevation layer by the mounds object and create a smaller elev object
• Plot the elev and mounds and the mounds_bb together.

# Load `sf` library
library(sf)

# Create `mounds` object from the shapefile
mounds <- st_read("C:/Users/alexp/Documents/School/Spatial_analytics/HW/HW3/cds-spatial/data/KAZ_mounds.shp")

Reading layer `KAZ_mounds' from data source 
  `C:\Users\alexp\Documents\School\Spatial_analytics\HW\HW3\cds-spatial\data\KAZ_mounds.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 773 features and 5 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 352481.3 ymin: 4712325 xmax: 371282.4 ymax: 4730029
Projected CRS: WGS 84 / UTM zone 35N

# Verify that `mounds` CRS matches the `elevation`
st_crs(mounds)

Coordinate Reference System:
  User input: WGS 84 / UTM zone 35N 
  wkt:
PROJCRS["WGS 84 / UTM zone 35N",
    BASEGEOGCRS["WGS 84",
        DATUM["World Geodetic System 1984",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4326]],
    CONVERSION["UTM zone 35N",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",0,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",27,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",0.9996,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",500000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    ID["EPSG",32635]]

st_crs(elevation)  #they match

Coordinate Reference System:
  User input: WGS 84 / UTM zone 35N 
  wkt:
PROJCRS["WGS 84 / UTM zone 35N",
    BASEGEOGCRS["WGS 84",
        DATUM["World Geodetic System 1984",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4326]],
    CONVERSION["UTM zone 35N",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",0,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",27,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",0.9996,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",500000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Engineering survey, topographic mapping."],
        AREA["Between 24°E and 30°E, northern hemisphere between equator and 84°N, onshore and offshore. Belarus. Bulgaria. Central African Republic. Democratic Republic of the Congo (Zaire). Egypt. Estonia. Finland. Greece. Latvia. Lesotho. Libya. Lithuania. Moldova. Norway. Poland. Romania. Russian Federation. Sudan. Svalbard. Turkey. Uganda. Ukraine."],
        BBOX[0,24,84,30]],
    ID["EPSG",32635]]

# Create a bounding box around the mounds
mounds_bb <- st_make_grid(mounds, n = 1)

# Plot the `mounds_bb` and the `mounds`
plot(mounds_bb)
plot(mounds, add = TRUE)

# Crop the `elevation` layer by the `mounds` object and create a smaller `elev`
# object
elev <- crop(elevation, mounds)

# Plot the `elev` and `mounds` and the `mounds_bb` together
plot(elev)
plot(mounds_bb, add = TRUE)
plot(mounds, add = TRUE)

Instructions II

• Create survey object from shapefile called “KAZ_units.shp”
• Project the survey object to match the new elev raster with st_transform()if needed.
• Compute the area of the survey with st_area() and save this object as areas. What units are these?
• Filter the survey units to only those above 30000 square meters with the filter() function. You will need to wrap areas in unclass(). Save as survey_big. Remember to have the tidyverse or dplyr library attached for filter() to work properly. Also sf might interfere so specify the dplyr::filter() if needed.

library(dplyr)

# Read in the survey object
survey <- st_read("C:/Users/alexp/Documents/School/Spatial_analytics/HW/HW3/cds-spatial/data/KAZ_units.shp")

Reading layer `KAZ_units' from data source 
  `C:\Users\alexp\Documents\School\Spatial_analytics\HW\HW3\cds-spatial\data\KAZ_units.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 7708 features and 25 fields (with 1 geometry empty)
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 352181.7 ymin: 4712923 xmax: 370892.1 ymax: 4729960
Projected CRS: WGS 84 / UTM zone 35N

# projection is not needed

# Compute the area of the survey
areas <- st_area(survey)
# assessing from the Environment menu areas are in sq meters

# Filter to survey with areas > 30000
survey_big <- dplyr::filter(survey, unclass(areas) > 30000)

Instructions III

• Review the plot of elev raster.
• Plot the geometry of the survey_big over it.
• Mask the elev layer with survey_big and save as elevation_mask. This may take a couple of seconds.
• Review the plot of elevation_mask.

# Plot the elevation raster
plot(elev)

# Plot the geometry of survey_big
plot(st_geometry(survey_big))

# Mask the elev layer with survey_big and save as elevation_mask
elevation_mask <- mask(elev, mask = survey_big)

# Plot elevation_mask -- this is a raster!
plot(elevation_mask)

Instructions

• Load the East half of the two IKONOS images of the Kazanlak Valley from (“KazE.tif”).
• Plot the file you read in with the appropriate function for a multi-band raster.
• Determine the raster resolution using res() and number of raster cells in the layer with ncell().
• Aggregate the IKONOS image using the default for fun and a factor of 10 and save the new raster to east_small.
• Plot the new raster with plot() for comparison to the old version.
• Determine the new raster resolution and the number of raster cells.

# Load the IKONOS raster
east <- brick("C:/Users/alexp/Documents/School/Spatial_analytics/HW/HW3/cds-spatial/data/KazE.tif")

# Plot the IKONOS raster
plot(east)

# Determine the raster resolution
res(east)

[1] 10 10

# Determine the number of cells
ncell(east)

[1] 2374596

# Aggregate the raster
east_small <- aggregate(east, fact = 10)

# Plot the new east layer
plot(east_small)

# Determine the new raster resolution
res(east_small)  #100 100 vs previous 10 10

[1] 100 100

# Determine the number of cells in the new raster
ncell(east_small)  #23940 vs previous 2374596

[1] 23940

Instructions

• Ensure mounds are still in memory.
• Use the raster function extract() to determine the elevation at each of the mounds. Assign the extracted value into a elev column in the mounds object. Beware that the extract() function exists across multiple packages, so it’s wise to use raster:: in front of it.
• Look at the mounds elevation column through the plot() function as well as histogram. Do the extract() results make sense? The elevation layer values represent meters above sea level.

# Extract the elevation values at the sites
mounds$elev <- raster::extract(elevation, mounds)

# Look at the mounds and extraction results
plot(mounds["elev"])

hist(mounds$elev)  #I don't see anything wrong with the results so far

Instructions

• Make sure elevation object exists in memory (“Aster.tif”, it is a single-band raster).
• Read in the prominence raster layer from “prominence.tif”; it is also a single-band raster.
• Plot the prominence object. Do the legend units make sense?
• Specify function f, where you look for elevation values over 400 msl and below 650m (mounds don’t appear above that elevation) and prominence values over 60%
• Call overlay() on elevation and prominence. Set the fun argument to f.
• If you have not cropped the two rasters to the same extent yet, you can do so inside the overlay() function

# Check in on the elevation and read in prominence layer
elevation

class      : RasterLayer 
dimensions : 2484, 2592, 6438528  (nrow, ncol, ncell)
resolution : 30, 30  (x, y)
extent     : 313183.7, 390943.7, 4676606, 4751126  (xmin, xmax, ymin, ymax)
crs        : +proj=utm +zone=35 +datum=WGS84 +units=m +no_defs 
source     : Aster.tif 
names      : Aster 
values     : -9999, 2347  (min, max)

prominence <- raster("C:/Users/alexp/Documents/School/Spatial_analytics/HW/HW3/cds-spatial/data/prominence1500m.tif")

# Plot prominence
plot(prominence)  # supposing the units are percentages as mentioned above, yes they make sense

# Function f with 2 arguments and the raster math code
f <- function(rast1, rast2) {
    rast1 < 650 & rast1 > 400 & rast2 > 60
}

# Align the extent of the two rasters with crop()
elev2 <- crop(elevation, prominence)

# Do the overlay using f as fun.
elevation_prom_overlay <- overlay(elev2, prominence, fun = f)


# Plot the result (low elevation and high prominence areas)
plot(elevation_prom_overlay)

elevation_prom_overlay

class      : RasterLayer 
dimensions : 1201, 1201, 1442401  (nrow, ncol, ncell)
resolution : 30, 30  (x, y)
extent     : 340153.7, 376183.7, 4700126, 4736156  (xmin, xmax, ymin, ymax)
crs        : +proj=utm +zone=35 +datum=WGS84 +units=m +no_defs 
source     : memory
names      : layer 
values     : 0, 1  (min, max)

Task 7 - HOMEWORK: Inspect the most prominent mounds

In the above exercise, we produced an elevation-prominence overlay. Mounds and other sites that sit in this overlay enjoy a strategic position vis-a-vis the rest of the valley. Which ones are they, however, and what are their real prominence values? It is hard to see at the scale of the valley and it would be good to pull the sites out.

Find the mounds that enjoy the most prominent locations as well as those that feature in the elevation-prominence overlay raster. Produce a list of the ID numbers (TRAP_Code) of all the overlay mounds and 25 most prominent mounds and plot them (expressing their prominence somehow) .

# Plot the `prom-el-overlay` and the `mounds` on top of each other
plot(elevation_prom_overlay)
plot(mounds, add = TRUE)

# same with `prominence` raster and `mounds`
plot(prominence)
plot(mounds, add = TRUE)  #I can't really see the mounds anymore, it's too cluttered

# Extract values from the elevation-prominence overlay and from the prominence
# at mounds and write them to two columns
mounds$prom_el_overlay <- raster::extract(elevation_prom_overlay, mounds)
mounds$prominence <- raster::extract(prominence, mounds)

# Make an object of mounds that sit within these strategic high-visibility
# locations
best_mounds <- dplyr::filter(mounds, prom_el_overlay == 1)
# taking all mounds where the value for prom_el_overlay was 1, meaning they had
# higher than 60% prominence and desired elevation as we filtered with f before

nrow(best_mounds)

[1] 132

# there is 132 mounds withing the most visible areas

# Make an object of 25 mounds with the highest prominence values
highest_prom_mounds <- mounds %>%
    arrange(desc(prominence)) %>%
    slice(1:25)

# writing out all the unique Trap codes included
unique(highest_prom_mounds$TRAP_Code)

 [1] 2002 3112 3105 4057 4058 3722 4059 3417 4056 3276 3024 1020 4087 1019 3026
[16] 2028 4066 5002 4086 1071 3275 2003 2004 3703 3420

library(mapview)
mapview(best_mounds, col.regions = "green") + mapview(highest_prom_mounds, col.regions = "red")

# we can see many of these points are actually overlapping so lets make it
# easier to see the overlapping ones

# create object for overlapping points
overlap <- st_join(best_mounds, highest_prom_mounds, left = FALSE)


# add the object to mapview
mapview(best_mounds, col.regions = "green") + mapview(highest_prom_mounds, col.regions = "red") +
    mapview(overlap, col.regions = "orange")

Task 8 - OPTIONAL HOMEWORK: Sum raster values across polygons - assessing prominence of past settlements

Previously you have extracted values from computed rasters at points. More often, you will need to extract values using more complex vector shapes where you will need to decide how to summarize the raster values that fall in. In this exercise, you will load the layer of ancient settlements for the Kazanlak Valley and sum the overlapping elevation_prom_overlay values within the settlement polygons. Then you will find out which of the sites contained at least 0.5 ha of strategic high-visibility area.

# Read in the site polygons
sites <- ______("data/KAZ_nuclei.shp")

# Plot the sites
plot(elevation_prom_overlay);plot(__________)


# Create a raster mask from the elevation_prom_overlay using the sites
sites_mask <- _____________

# Crop the raster mask to the sites
sites_strat <- _____________

# Plot and compare the results
plot(sites_mask)
plot(sites_strat)


# Produce a list of the most prominent sites